Breast cancer is a very serious malignant tumor originating from the breast cells. The disease occurs generally in women, but also men can rarely have it. During the prognosis of breast cancer, abnormal growth of cells in breast takes place and this growth can be in two types which are benign (non-cancerous) and malignant (cancerous). In this study, the aim is to diagnose the breast cancer using various intelligent techniques including Decision Trees (DT), Support Vector Machines (SVM), Artificial Neural Network (ANN) and also the ensemble of these techniques. Experimental studies were done using SPSS Clementine software and the results show that the ensemble model is better than the individual models according to the evaluation metric which is the accuracy. In order to increase the efficiency of the models, feature selection technique is applied. Moreover, models are also analyzed in terms of other error measures like sensitivity and specificity.